SKU: 53726251092

Oil Pump 41314067 for Perkins Engine 6.354 T6.354 6.3541 T6.3541 6.3542 Massey Ferguson 1100 1105 1130 1135 Tractor

$110.57 $122.86
Save 10%
No.2 Bestseller on mapetitemediatheque.fr >

Pay in installments of $30.71 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Aug 4 - Aug 9

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

Oil Pump 41314067 for Perkins Engine 6.354 T6.354 6.3541 T6.3541 6.3542 Massey Ferguson 1100 1105 1130 1135 TractorPart Number: 4131406741314067 replaces 41314062 since 27 03 1982. 41314067 replaces 41314058 since 15 08 1980. Massey Ferguson Industrial: 736012M91 736012M91 Application: For Perkins Engine 6. 354 T6. 354 6. 3541 T6. 3541 6. 3542Engine Type : TC Company name : 6. 354TC00237 TC00306 TC00372 TC00373 TC00390 TC00391 TC07653 TC07939 TC13575 TC13575LX TC13607 TC13607LX TC20243 TC20459 TC20571 TC20651 TC20928 TC20959 TC21020 TC21047 TC21192 TC21321 TC21375

Part Number:

4131406741314067 replaces 41314062 since 27/03/1982.41314067 replaces 41314058 since 15/08/1980.Massey Ferguson - Industrial:736012M91 736012M91

Application:


For Perkins Engine 6.354 T6.354 6.3541 T6.3541 6.3542Engine Type : TC Company name : 6.354TC00237 TC00306 TC00372 TC00373 TC00390 TC00391 TC07653 TC07939 TC13575 TC13575LX TC13607 TC13607LX TC20243 TC20459 TC20571 TC20651 TC20928 TC20959 TC21020 TC21047 TC21192 TC21321 TC21375 TC21641 TC21884 TC21885 TC22042 TC22042LX TC22112 TC22266 TC22266RX TC22276 TC22276LX TC22460 TC22544 Engine Type : TDTD00245 TD00307 TD07573 Engine Type : TE Company name : T6.354TE00262 TE00272 TE00273 TE00278 TE00302 TE00303 TE08861 TE09846 TE09847 TE20572 TE20596 TE20597 TE20600 TE20696 TE20773 TE20779 TE20779LX TE21286 TE21287 TE21925 TE22018 TE22097 TE22364 Engine Type : TFTF00263 TF00279 TF00320 TF00321 TF08862 TF20702 TF21508 TF21509 Engine Type : TG Company name : 6.3541TG00360 TG21303 TG21303LX TG21428 TG21855 TG22453 TG22550 TG22572 TG22577 TG22577LX TG22585 TG22819 Engine Type : TH Company name : T6.3541TH00308 TH00356 TH00359 TH21449 TH21722 TH21794 TH22180 TH22282 TH22431 TH22481 TH22571 Engine Type : TJ Company name : 6.3542TJ00286 TJ00287 TJ00288 TJ00292 TJ00322 TJ00361 TJ00363 TJ21611 TJ21790 TJ22034 TJ22083 TJ22189 TJ22256 TJ22413 TJ22414 TJ22414LX TJ22461 TJ22482 TJ22491 TJ22559 TJ22563 Engine Type : TKTK21871 TK22384 Engine Type : TRTR00300 TR00309 TR00362 TR21272 TR21376 TR21376LX TR21776 TR21777 TR22597 Engine Type : TTTT21724 Fit Massey Ferguson Combine Models: 740, 750, 760, (750, 760 (E-SN UA48970L>) T6.354.1 / TC6.354.1 Diesel)Fit Massey Ferguson Tractor Models: (1100, 1105, 1130, 1135 (W/ A6.354 PERKINS DIESEL ENGINE)), (1130 (E-SN <354UA3355T) T6.354 Diesel), (1105, 1130 (E-SN 354UA3355T>), 1135 T6.354.1 / TC6.354.1 Diesel), (1100 (E-SN <354UA10361), 1100 (E-SN 354UA10362>) 6.354 Diesel)

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 53726251092

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.5 ★★★★★
Based on 22 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Fort Morgan, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Lake Worth, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
West Palm Beach, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Cuba, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Belleville, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

recommand products

7/16 Hex Dr. Bit XZN M12 Triple Square Length 35mm
16.34
1 Sq. Dr. Socket 1.3/32 6 point Length 57mm
60.86
1 Sq. Dr. Socket 13/16 6 point Length 57mm
63.07
Hot-Shot® Saber Six Red Motor SS1
69.99
1 Sq. Dr. Socket 55mm 6 point Length 75mm
111.68